From a Comprehensive Experimental Survey to a Cost-based Selection Strategy for Lightweight Integer Compression Algorithms

Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding and null suppression has considerably enlarged the corpus of available algorithms. As a result, today there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. However, a comparative evaluation of these algorithms with different data and hardware characteristics has never been sufficiently conducted in the literature. To close this gap, we conducted an exhaustive experimental survey by evaluating several state-of-the-art lightweight integer compression algorithms as well as cascades of basic techniques. We systematically investigated the influence of data as well as hardware properties on the performance and the compression rates. The evaluated algorithms are based on publicly available implementations as well as our own vectorized reimplementations. We summarize our experimental findings leading to several new insights and to the conclusion that there is no single-best algorithm. Moreover, in this article, we also introduce and evaluate a novel cost model for the selection of a suitable lightweight integer compression algorithm for a given dataset

An Automatic Physical Design Tool for Clustered Column-Stores

Good database design is typically a very difficult and costly process. As database systems get more complex and as the amount of data under management grows, the stakes increase accordingly. Past research produced a number of design tools capable of automatically selecting secondary indexes and materialized views for a known workload. However, a significant bulk of research on automated database design has been done in the context of row-store DBMSes. While this work has produced effective design tools, new specialized database architectures demand a rethinking of automated design algorithms. In this paper, we present results for an automatic design tool that is aimed at column-oriented DBMSes on OLAP workloads. In particular, we have chosen a commercial column store DBMS that supports data sorting. In this setting, the key problem is selecting proper sort orders and compression schemes for the columns as well as appropriate pre-join views. This paper describes our automatic design algorithms as well as the results of some experiments using it on realistic data sets

An Automatic Physical Design Tool for Clustered Column-Stores

ABSTRACT Good database design is typically a very difficult and costly process. As database systems get more complex and as the amount of data under management grows, the stakes increase accordingly. Past research produced a number of design tools capable of automatically selecting secondary indexes and materialized views for a known workload. However, a significant bulk of research on automated database design has been done in the context of row-store DBMSes. While this work has produced effective design tools, new specialized database architectures demand a rethinking of automated design algorithms. In this paper, we present results for an automatic design tool that is aimed at column-oriented DBMSes on OLAP workloads. In particular, we have chosen a commercial column store DBMS that supports data sorting. In this setting, the key problem is selecting proper sort orders and compression schemes for the columns as well as appropriate pre-join views. This paper describes our automatic design algorithms as well as the results of some experiments using it on realistic data sets

Physical Design for Non-relational Data Systems

Decades of research have gone into the optimization of physical designs, query execution, and related tools for relational databases. These techniques and tools make it possible for non-expert users to make effective use of relational database management systems. However, the drive for flexible data models and increased scalability has spawned a new generation of data management systems which largely eschew the relational model. These include systems such as NoSQL databases and distributed analytics frameworks such as Apache Spark which make use of a diverse set of data models. Optimization techniques and tools developed for relational data do not directly apply in this setting. This leaves developers making use of these systems with the need to become intimately familiar with system details to obtain good performance. We present techniques and tools for physical design for non-relational data systems. We explore two settings: NoSQL database systems and distributed analytics frameworks. While NoSQL databases often avoid explicit schema definitions, many choices on how to structure data remain. These choices can have a significant impact on application performance. The data structuring process normally requires expert knowledge of the underlying database. We present the NoSQL Schema Evaluator (NoSE). Given a target workload, NoSE provides an optimized physical design for NoSQL database applications which compares favourably to schemas designed by expert users. To enable existing applications to benefit from conceptual modeling, we also present an algorithm to recover a logical model from a denormalized database instance. Our second setting is distributed analytics frameworks such as Apache Spark. As is the case for NoSQL databases, expert knowledge of Spark is often required to construct efficient data pipelines. In NoSQL systems, a key challenge is how to structure stored data, while in Spark, a key challenge is how to cache intermediate results. We examine a particularly common scenario in Spark which involves performing iterative analysis on an input dataset. We show that jobs written in an intuitive manner using existing Spark APIs can have poor performance. We propose ReSpark, which automates caching decisions for iterative Spark analyses. Like NoSE, ReSpark makes it possible for non-expert users to obtain good performance from a non-relational data system